Head of AI and Machine Learning Engineering

United States Digital Space LLC; Denver (NY); Hybrid; USD 250,000 - 360,000; Full time

Job summary

United States Digital Space LLC is seeking a strategic Head of AI/MLE to lead a broad organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists. You will partner with senior leaders to shape where AI can create durable customer and business impact.

You will unify classical ML and GenAI into a coherent strategy, mature the platform for self-service adoption, and ensure production systems meet high standards for reliability, safety, and governance.

Qualifications

• 10+ years leading teams in applied ML, AI, engineering, or data science.
• Deep technical expertise across AI/ML systems, GenAI, risk modeling, and production-scale deployment.
• Strong software engineering and systems background, with the ability to lead technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.
• Experience leading and scaling high-performing technical organizations, including Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.
• Experience evolving ML teams toward a stronger software engineering and systems orientation, with clear ownership for building, operating, and improving production AI/ML systems.
• Strong platform orientation, with experience building tools, primitives, guardrails, and self-service capabilities that help product and engineering teams build AI/ML-powered products safely and effectively.
• Executive-level strategic judgment, with the ability to shape company-level AI/ML priorities, align senior leaders around tradeoffs, and make clear investment decisions based on customer value, business impact, technical feasibility, risk, data readiness, and operational complexity.
• Strong executive communication and influence, with the ability to explain complex AI/ML concepts and technical decisions in a way that clarifies strategy, tradeoffs, risk, investment needs, and organizational implications.
• Experience operating as a peer to senior cross-functional leaders across product, engineering, design, data, risk, legal, security, and business teams — bringing clarity, urgency, and practical judgment to ambiguous company-level opportunities.
• A clear thesis on how classical ML and GenAI should work together, how modern AI platform capabilities like retrieval, evaluation, agents, and observability should come together, and how AI/ML teams should evolve as the field becomes more software- and systems-oriented.
• Experience in fintech, risk modeling, regulated environments, or domains with high standards for reliability, trust, and compliance is a plus.
• Advanced degree in computer science, data science, machine learning, statistics, or a related field is a plus, but demonstrated systems leadership, production judgment, and executive-lev

Responsibilities

• Lead and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.
• Define and execute the company’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports the company’s broader business and product goals.
• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.
• Shape how AI-native products and internal systems are built at the company, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.
• Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.
• Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.
• Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for quality, operational rigor, and business accountability.
• Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.
• Stay close to the frontier of AI/ML advancement and help the company apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.

Skills

AI leadership

Machine Learning

Software engineering

Executive communication

Cross-functional collaboration

Education

Advanced degree in CS/related field

Tools

ML Platform tooling

Risk data science tools

Job description

About the company

At the company, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build the company should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at the company. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

the company sits at the center of many of the most important workflows for small businesses, which creates a meaningful opportunity to use rich product and customer data to build AI- and ML-powered systems that improve customer experiences, automate complex work, support better decision-making, and help small businesses thrive. As the company becomes more AI-native, we are evolving how AI, ML, risk modeling, and platform capabilities come together across our products and internal systems.

We are seeking a strategic Head of AI/MLE to lead this next chapter. In this key leadership role, you will define how the company builds, deploys, evaluates, and scales AI/ML systems across the company. You will lead a broad organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, while partnering closely with senior business leaders to shape where AI can create durable customer and business impact.

As the Head of AI/MLE at the company, you will be responsible for unifying classical ML and GenAI into a coherent technical strategy, maturing the platform for broader self-service adoption, and shaping how AI-native products and production systems are built at the company. Your teams will help translate business problems into end-to-end AI/ML systems, from experimentation and prototyping through evaluation, deployment, monitoring, feedback loops, and operational governance.

Your leadership will be critical in setting the technical direction, operating model, and quality bar for AI at the company. You will help teams move quickly where speed and learning matter most, while ensuring production systems meet high standards for reliability, measurement, safety, and long-term maintainability. This is a senior technical executive role for someone who can combine deep engineering credibility, strong business judgment, and executive-level influence to make AI a durable advantage for the company.

Here’s what you’ll do day-to-day:

• Lead, manage, and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.

• Define and execute the company’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports the company’s broader business and product goals.

• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.

• Shape how AI-native products and internal systems are built at the company, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.

• Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.

• Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.

• Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for quality, operational rigor, and business accountability.

• Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.

• Stay close to the frontier of AI/ML advancement and help the company apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.

Here’s what we’re looking for:

• We love meeting people with different technical and leadership backgrounds. For this role, we are looking for 10+ years of experience leading teams in applied machine learning, AI, engineering, or data science roles, with a track record of delivering impactful customer-facing software solutions.

• Deep technical expertise across AI/ML systems, including classical ML, GenAI/LLMs, statistical modeling, risk modeling, and production-scale deployment.

• Strong software engineering and systems background, with the ability to lead technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.

• Experience leading and scaling high-performing technical organizations, including Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.

• Experience evolving ML teams toward a stronger software engineering and systems orientation, with clear ownership for building, operating, and improving production AI/ML systems.

• Strong platform orientation, with experience building tools, primitives, guardrails, and self-service capabilities that help product and engineering teams build AI/ML-powered products safely and effectively.

• Executive-level strategic judgment, with the ability to shape company-level AI/ML priorities, align senior leaders around tradeoffs, and make clear investment decisions based on customer value, business impact, technical feasibility, risk, data readiness, and operational complexity.

• Strong executive communication and influence, with the ability to explain complex AI/ML concepts and technical decisions in a way that clarifies strategy, tradeoffs, risk, investment needs, and organizational implications.

• Experience operating as a peer to senior cross-functional leaders across product, engineering, design, data, risk, legal, security, and business teams — bringing clarity, urgency, and practical judgment to ambiguous company-level opportunities.

• A clear thesis on how classical ML and GenAI should work together, how modern AI platform capabilities like retrieval, evaluation, agents, and observability should come together, and how AI/ML teams should evolve as the field becomes more software- and systems-oriented.

• Experience in fintech, risk modeling, regulated environments, or domains with high standards for reliability, trust, and compliance is a plus.

• Advanced degree in computer science, data science, machine learning, statistics, or a related field is a plus, but demonstrated systems leadership, production judgment, and executive-lev